Amazon Aurora to Tableau

This page provides you with instructions on how to extract data from Amazon Aurora and analyze it in Tableau. (If the mechanics of extracting data from Amazon Aurora seem too complex or difficult to maintain, check out Stitch, which can do all the heavy lifting for you in just a few clicks.)

What is Amazon Aurora?

Amazon Aurora is a MySQL-compatible relational database employed by organizations that are looking for better performance than they can get from MySQL at cost-effective price points. Aurora is best used as a transactional or operational database and not for analytics.

What is Tableau?

Tableau is one of the world's most popular analysis platforms. The software helps companies model, explore, and visualize their data. It also offers cloud capabilities that allow analyses to be shared via the web or company intranets, and its offerings are available as both installed software and as a SaaS platform. Tableau is widely known for its robust and flexible visualization capabilities, which include dozens of specialized chart types.

In addition to its business software, Tableau also offers a free product called Tableau Public for analyzing open data sets. If you're new to Tableau, this offering is a great way to experience Tableau's capabilities at no cost and share your work publicly.

Getting data out of Amazon Aurora

Aurora provides several methods for extracting data; the one you use may depend upon your needs and skill set.

The most common way to get data out of any database is simply to write queries. SELECT queries allow you to pull the data you want. You can specifying filters and ordering, and limit results.

If you’re looking to export data in bulk, there may be an easier way. A handy command-line tool called mysqldump allows you to export entire tables and databases in a format you specify (i.e. delimited text, CSV, or SQL queries that would restore the database if run).

Preparing Amazon Aurora data

For every table in your Amazon Aurora database, you'll need a corresponding table in your destination database. Make sure you've pinpointed all of the fields that will be inserted into your destination, and determined the datatypes for each object (i.e. INTEGER, DATETIME, etc.) to make sure they are mapped properly when they get inserted into the new table.

Loading data into Tableau

Analyzing data in Tableau requires putting it into a format that Tableau can read. Depending on the data source, you may have options for achieving this goal, but the best practice among most businesses is to build a data warehouse that contains the data, and then connect that data warehouse to Tableau.

Tableau provides an easy-to-use Connect menu that allows you to connect data from flat files, direct data sources, and data warehouses. In most cases, connecting these sources is simply a matter of creating and providing credentials to the relevant services.

Once the data is connected, Tableau offers an option for locally caching your data to speed up queries. This can make a big difference when working with slower database platforms or flat files, but is typically not necessary when using a scalable data warehouse platform. Tableau's flexibility and speed in these areas are among its major differentiators in the industry.

Analyzing data in Tableau

Tableau's report-building interface may seem intimidating at first, but it's one of the most powerful and intuitive analytics UIs on the market. Once you understand its workflow, it offers fast and nearly limitless options for building reports and dashboards.

If you're familiar with Pivot Tables in Excel, the Tableau report building experience may feel somewhat familiar. The process involves selecting the rows and columns desired in the resulting data set, along with the aggregate functions used to populate the data cells. Users can also specify filters to be applied to the data and choose a visualization type to use for the report.

You can learn how to build a report from scratch for free (although a sign-in is required) from the Tableau documentation.

Keeping Amazon Aurora data up to date

At this point you’ve coded up a script or written a program to get the data you want and successfully moved it into your data warehouse. But how will you load new or updated data? It's not a good idea to replicate all of your data each time you have updated records. That process would be painfully slow and resource-intensive.

Instead, identify key fields that your script can use to bookmark its progression through the data and use to pick up where it left off as it looks for updated data. Auto-incrementing fields such as updated_at or created_at work best for this. When you've built in this functionality, you can set up your script as a cron job or continuous loop to get new data as it appears in Aurora.

And remember, as with any code, once you write it, you have to maintain it. If Aurora sends a field with a datatype your code doesn't recognize, you may have to modify the script. If your users want slightly different information, you definitely will have to.

From Amazon Aurora to your data warehouse: An easier solution

As mentioned earlier, the best practice for analyzing Amazon Aurora data in Tableau is to store that data inside a data warehousing platform alongside data from your other databases and third-party sources. You can find instructions for doing these extractions for leading warehouses on our sister sites Amazon Aurora to Redshift, Amazon Aurora to BigQuery, and Amazon Aurora to Snowflake.

Easier yet, however, is using a solution that does all that work for you. Products like Stitch were built to solve this problem automatically. With just a few clicks, Stitch starts extracting your Amazon Aurora data via the API, structuring it in a way that is optimized for analysis, and inserting that data into a data warehouse that can be easily accessed and analyzed by Tableau.